Electricity Theft Detection in Smart Grids Based on Deep Neural Network

IEEE BASE PAPER ABSTRACT:

Electricity theft is a global problem that negatively affects both utility companies and electricity users. It destabilizes the economic development of utility companies, causes electric hazards and impacts the high cost of energy for users. The development of smart grids plays an important role in electricity theft detection since they generate massive data that includes customer consumption data which, through machine learning and deep learning techniques, can be utilized to detect electricity theft. This paper introduces the theft detection method which uses comprehensive features in time and frequency domains in a deep neural network-based classification approach. We address dataset weaknesses such as missing data and class imbalance problems through data interpolation and synthetic data generation processes. We analyze and compare the contribution of features from both time and frequency domains, run experiments in combined and reduced feature space using principal component analysis and finally incorporate minimum redundancy maximum relevance scheme for validating the most important features. We improve the electricity theft detection performance by optimizing hyperparameters using a Bayesian optimizer and we employ an adaptive moment estimation optimizer to carry out experiments using different values of key parameters to determine the optimal settings that achieve the best accuracy. Lastly, we show the competitiveness of our method in comparison with other methods evaluated on the same dataset. On validation, we obtained 97% area under the curve (AUC), which is 1% higher than the best AUC in existing works, and 91.8% accuracy, which is the second-best on the benchmark.

PROJECT OUTPUT VIDEO:

ALGORITHM / MODEL USED:

Artificial Neural Network (ANN).

OUR PROPOSED ABSTRACT:

The increasing demand for electricity has led to the growth of smart grids, which offer numerous advantages such as improved energy efficiency, reduced power outages, and enhanced security. However, one of the significant challenges in smart grids is electricity theft, which is a major cause of revenue loss for utility companies. So, electricity theft is a major concern for electric power distribution companies.

The aim of this project is to develop an effective approach for detecting electricity theft in smart grids based on Artificial Neural Network (ANN). The proposed approach will use electricity usage dataset which is referred from the popular web repository kaggle. The collected data will be preprocessed and fed into the ANN, which will learn to identify patterns and anomalies in the consumption data. The ANN model will be trained using a dataset of legitimate consumption patterns and then tested with data that contains instances of electricity theft. To evaluate the performance of the proposed approach, the model will be tested on a test data.

The results predicted from our proposed system of electricity theft detection in smart grids using ANN is Good. Our system achieved Training Accuracy of 99% and Validation Accuracy of 99%. The performance metrics used will include accuracy, precision, recall, and F1-score. We also developed the proposed system in Flask Web framework for easy usage with better User Interface for the predicting the results.

The expected outcome of this project is an effective approach for detecting electricity theft in smart grids using ANN, which can be used by utility companies to improve their revenue collection and enhance the security of the smart grid. This project can also be extended to other domains that involve anomaly detection in large-scale datasets, such as fraud detection in financial systems and intrusion detection in computer networks.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

System : Pentium i3 Processor.
Hard Disk : 500 GB.
Monitor : 15’’ LED
Input Devices : Keyboard, Mouse
Ram : 4 GB

SOFTWARE REQUIREMENTS:

Operating System : Windows 10 / 11.
Coding Language : Python 3.8.
Web Framework : Flask.
Frontend : HTML, CSS, JavaScript.

REFERENCE:

LELOKO J. LEPOLESA, SHAMIN ACHARI, AND LING CHENG, (Senior Member, IEEE), “Electricity Theft Detection in Smart Grids Based on Deep Neural Network”, IEEE Access (Volume: 10), 2022.

Python IEEE Projects

Electricity Theft Detection in Smart Grids Based on Deep Neural Network